To improve assembly quality and efficiency, a method based on deep learning and object matching is proposed to detect missing and wrong parts. An improved YOLO V3 neural network is designed to solve the problem of mis...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
To improve assembly quality and efficiency, a method based on deep learning and object matching is proposed to detect missing and wrong parts. An improved YOLO V3 neural network is designed to solve the problem of missing assembly. A small target detection scale and attention module is added to the neural network. the size of prior anchor box is optimized by K-means++ clustering algorithm. For the problem of wrong assembly, the standard assembly state detection template is constructed according to the virtual assembly scene in CAD software, and the 2D detection box of the current assembly object in the scene image is matched withthe 2D box in the standard state template based on IoU (Intersection over Union) calculation. the assembly model MONA (a 3D model for the evaluation of manual Assembly tasks), is used to test the proposed method. Experimental results show that this method can accurately locate and identify assembly parts, and effectively detect the missing and wrong parts in the assembly process.
thanks to the rapid development of information technology, the concept of smart manufacturing has been widely promoted in recent years, and gave birth to a large number of industrial software. the large amount of data...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
thanks to the rapid development of information technology, the concept of smart manufacturing has been widely promoted in recent years, and gave birth to a large number of industrial software. the large amount of data generated and accumulated in industrial software has the characteristics of distributed, multi format and complex relationship. the traditional relational database cannot complete the unified description of these data and respond to their rapid changes. Dataspace is a new data management method that can deal with multi-source heterogeneous data and update dynamically. therefore, this paper determines the requirements of industrial system data integration. Based on this, a construction and update process of industrial dataspace is proposed. Metadata extraction is used to complete the unified description of data, and then through the construction of data relations and data-service mapping, multi-source heterogeneous data can be effectively organized. Finally, the effectiveness of this method is evaluated through a case study in a nuclear power enterprise.
Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) ta...
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In self-adaptive software systems, generic controllers can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that can be provided by ea...
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ISBN:
(纸本)9798350311921
In self-adaptive software systems, generic controllers can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that can be provided by each of the stages of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. this paper advocates controllers that are structurally flexible, and which are composed from micro-controllers, each providing specific services (e.g., based on microservices). To provide evidence on the feasibility of our approach, we compare three different architectural configurations for the controller: monolithic, decentralised, and decentralised with a meta-controller. Results from our experiments indicate that even though the decentralised configuration with a meta-controller demanded more computational resources, it performed comparatively well when compared to the other configurations. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time, and may promote reuse at development-time.
Indoor occupancy is an important factor in optimizing HVAC control and predicting airborne disease risk. Occupancy estimation is based on carbon dioxide concentration. Indoor carbon dioxide concentration is affected b...
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software Product Line (SPL) engineering relies on explicitly documenting and managing variability information, typically in variability models such as feature models, and relating them with reusable artifacts, e.g., s...
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ISBN:
(纸本)9798400705939
software Product Line (SPL) engineering relies on explicitly documenting and managing variability information, typically in variability models such as feature models, and relating them with reusable artifacts, e.g., software components. Many companies still rely on clone-and-own reuse approaches and could benefit from adopting an SPL approach instead. However, extracting variability information from existing systems is often challenging due to their size and complexity. Also, manually creating and maintaining variability models and relating them with reusable artifacts is very expensive and requires expert knowledge. To address this problem, SPL reverse-engineering approaches try to automatically extract variability information from existing systems to populate variability models with it. Unfortunately, many existing approaches are limited to a single artifact type and only a few more widely applicable methods have been proposed. In this short paper, we present our vision of a flexible, extensible and artifact-independent approach, which provides a framework for users to mine the variability of their existing system variants and automatically reverse-engineer SPLs. We discuss challenges and give an overview of a possible architecture as well as the steps necessary to realize our approach.
Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to knowledge Graphs, such as knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to hall...
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ISBN:
(纸本)9783031711695;9783031711701
Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to knowledge Graphs, such as knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or output results in a non-deterministic manner, thus leading to wrongly reasoned responses, even if they satisfy the user's demands. To highlight opportunities and challenges in knowledge graphs-related tasks, we experiment withthree distinguished LLMs, namely Mixtral-8x7b-Instruct-v0.1, GPT-3.5-Turbo-0125 and GPT-4o, on knowledge Graph Completion for static knowledge graphs, using prompts constructed following the TELeR taxonomy, in Zero- and One-Shot contexts, on a Task-Oriented Dialogue system use case. When evaluated using both strict and flexible metrics measurement manners, our results show that LLMs could be fit for such a task if prompts encapsulate sufficient information and relevant examples.
Designing is the most difficult task in software production and development. Researchers are trying to add automation to the software design and development process. this is because of the daily increase of demanding ...
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In this paper, we conducted research on the digital twin simulation technology of virtual battlefield and virtual equipment, and proposed a software testing system for airborne photodetection system based on digital t...
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Recommender systems are a crucial component in personalized information retrieval, withtheir performance directly impacting user experience. However, traditional recommender systems often suffer from limitations in h...
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